Managing Polypharmacy: Key to Predicting and Preventing Hospital Readmissions?

According to a recent Kaiser Health News report, 20% of Medicare patients hospitalized with one of five conditions (heart failure, heart attack, pneumonia, chronic lung problems or elective hip or knee replacements) return to the hospital within a month of discharge.1  Medicare penalizes hospitals if they have higher 30-day readmission rates than predicted by a Medicare formula based on the hospital’s mix of patients with any of the five conditions listed above and on the overall nation-wide readmission rates during the period July 2011 – June 2014. More than half of the nation’s hospitals were penalized by Medicare  in October because they exceeded their predicted hospital readmission rates. Some hospitals do better than others and some states do better than others. Eleven states have unacceptable readmission rates in more than three quarters of their hospitals. Sens. Manchin (D-W.Va) and Wicker (R-Miss) argue that socioeconomic factors should be considered in assessing Medicare penalties; they’ve introduced a bill in the Senate to this effect. There must be additional reasons, however, because other states with underserved populations have lower hospital readmission rates and fewer penalized hospitals (e.g. Maine at 31%, Alaska at 33%). What do Connecticut and New Jersey have in common that results in 90% and 97 % of their hospitals, respectively, penalized for their high readmission rates?  Both CT and NJ are among the top three highest per capita income states; the others are DC and MD.  Could “more” care result in higher readmission rates in these high income states?

A recent study by Picker and colleagues published in BMC Health Services Research provides a clue.2 The researchers used a retrospective cohort study design to assess predictors of readmissions among 5,507 patients, of whom 21% were readmitted within thirty days. They found that the number of discharge medications was significantly associated with the risk of hospital readmission within thirty days. For example, 18% of patients with 4 to 6 discharge medications and 34% of patients with more than 12 discharge medications were readmitted within 30 days. A predictive model and corresponding risk score for 30 day readmission was developed and included: a) more than 6 discharge medications (1 point), b) more than one emergency department (ED) visit in the prior six months before hospitalization (2 points), c) low hemoglobin value (< 9 gm/dl) (1.5 points), and d) diagnosis of heart failure (1.5 points), peripheral vascular disease (1 point) and, or, metastatic disease (1 point).  They found that a quarter of patients with a risk score between 1 and 5 and 35% of patients with a risk score > 5 were readmitted within 30 days. The authors concluded that their predictive model reflects the medical complexity of the patient – and it is medical complexity which ultimately drives hospital readmission.

Heart failure is a good example of this medical complexity. Applying Piker’s risk score, an individual with heart failure (1.5 points) who had one ED visit in the prior six months before hospitalization (2 points) and is discharged on more than 6 medications (1 point) would have a risk score of 4.5. But what does that ‘more than 6 medications’ look like in a real life clinical situation interviewing patients?   To find out, Knecht3 used the ActualMeds Medication Management System to interview 41 Connecticut patients with heart failure who were recently discharged from homecare following a hospital admission. The patients reported taking an average of 12.6 medications (all took at least 5), many with multiple dosing times. Self-medication with OTCs accounted for 29% of medications. Most patients (71%) were taking medications on the Beers list of potentially harmful drugs in older adults and 48% had related symptoms. Nearly all (95%) were taking medications with anticholinergic side effects and more than half had a high anticholinergic burden score4  thus increasing their risk of cognitive impairment and falls. These results add a lot of granularity to the Picker data: “more than 6” may really be average of twelve medications for patients with heart failure, and it’s not just the number of medications being taken, but medications which pre-dispose patients to  adverse drug events (ADEs) coming full circle in causing unplanned (and unpaid for) readmissions.  To prevent those re-admissions and even the original admissions in the first place, we need to look seriously at ALL of the medications that a patient is taking, and focus our energy on identification and stratification of patients who are at risk for ADEs.

ActualMeds offers a proprietary suite of evidence-based tools to stratify patients at greatest risk for adverse drug events (ADEs) and resulting emergency visits, hospitalization and readmission.  Separate data channels show risks as assessed in the EHR, medication claims, and patient interview. ActualMeds tracks prescriptions by prescriber and also tracks patient self-medication with OTCs, herbal preparations and supplements.  The ActualMeds dashboard provides visual heuristics that reveal at a glance those patients taking the most medications and the most complicated regimens.   Risks are exposed relative to important drug-drug interactions and  quality and patient safety measures such as the Beers’ Criteria medications and CMS STARS alerts.  An intuitive workflow enables the system to be used by all levels of licensure; making ActualMeds powerful analytics actionable for the patient’s entire health care team.

 

  1. Rau, J. Half of nation’s hospitals fail again to escape Medicare’s readmission penalties. Kaiser Health News. August 3. 2015. http://khn.org/news/half-of-nations-hospitals-fail-again-to-escape-medicares-readmission-penalties/
  2. Picker, D., Heard, K., Bailey, T.C., Martin, N.R., LaRossa, G.N. et al. The number of discharge medications predicts thirty-day hospital readmission: a cohort study. BMC Health Services Research. 2015;15:282 http://www.biomedcentral.com/1472-6963/15/282
  3. Knecht, J., Neafsey, P.J. The Gerontologic considerations and anticholinergic burden of the medication regime of patients living with heart failure:The gerontological considerations and anticholinergic burden.. Journal of Cardiovascular Nursing. Published online ahead of print. Dec. 7, 2015. http://journals.lww.com/jcnjournal/Abstract/publishahead/The_Medication_Regimen_of_Patients_With_Heart.99575.aspx
  4. Cai, X., Campbell, N., Khan, B., Callahan, C., & Boustani, M. Long-term anticholinergic use and the aging brain. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2013; 9(4):377-385.

 

Patricia J Neafsey, PhD (pharmacology): Co-founder and Chief Scientific Officer, ActualMeds Corporation.  Professor Emeritus, University of Connecticut School of Nursing. @PharmacoQueen

On February 22nd, 2016, posted in: Polypharmacy by

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